Interactive Tools for Science Advice

Ocean and Ecosystem Science Seminar Series | BIO | 2019-11-06

Background

The greatest value of a picture is when it forces us to notice
what we never expected to see.
- Tukey (1977)

Discovery is one of the most exciting parts of science
Visualizations are one of the tools we use to reveal patterns
Models allow us to simplify and describe these patterns
At various stages, we need to communicate our findings

Communicating information

Simplified workflow:

Data

\(\rightarrow\)

Models

\(\rightarrow\)

Discuss

Connections may not come easy
- Growing volume and variety of data
- Increasingly complex models
- Diverse backgrounds

Stock assessments

Communicating stock assessment results isn’t getting easier
- Growing volume and variate of data
- Increasingly complex models
- Diverse backgrounds
Traditional formats sometimes
- Overwhelm participants with endless tables and figures
- Fail to convey the richness of information available

Interactive tools

Simplify communication and improve accessibility
Common tools that are used daily on a range of websites
Their use is no longer restricted to website developers
- Integrated into software commonly used by the research community
- Surprisingly easy to generate interactive dashboards
- Growing number of users

Objective

Using examples we aim to demonstrate that interactive tools can simplify our workflow, from data and model exploration to communication
Examples:
1. RStrap Explorer: A tool for examining fishery-independent survey data
2. Tag Explorer: A mapping tool developed for the exploration of a long-term tagging study
3. NCAM Explorer: A tool for exploring results from the Northern Cod Assessment Model

RStrap Explorer

Fisheries-independent surveys have become a mainstay in stock assessment
Stratified-random surveys have been conducted by DFO for > 40 years
Typically analyzed using stratified analyses (RStrap in NL region)

Canadian Coast Guard | Twitter

RStrap Explorer

Running a series of stratified analysis can be cumbersome

library(Rstrap)

## Load survey data
load("converted_set_details_2017-04-27.Rdata")
load("converted_length-frequency_data_2017-04-27.Rdata")
load("age-growth_data_2017-04-27.Rdata")

## 3NO Witch Flounder
witch <- strat.fun(setdet = setdet, data.series = "Campelen", 
                   species = 890, survey.year = 1995:2012, 
                   season = "spring", NAFOdiv = c("3N", "3O"))

## 3K Roughhead Grenadier (length disaggregated)
grenadier <- strat.fun(setdet = setdet, lf = lf, data.series = "Campelen", 
                       program = "strat2 & strat1", species = 474, survey.year = 2012, 
                       season = "fall", NAFOdiv = "3K", strat = 622:654, sex = "unsexed", 
                       length.group = 0.5, group.by = "length")

## 2J3KL Greenland Halibut (length and age disaggregated)
turbot <- strat.fun(setdet = setdet, lf = lf, ag = ag, data.series = "Campelen", 
                    program = "strat2 & strat1", species = 892, survey.year = 1998:2012, 
                    season = "fall", NAFOdiv = c("2J", "3K", "3L"), 
                    sex = c("male","female","unsexed"), length.group = 1, 
                    group.by = "length & age")

## 3Ps Atlantic Cod (length and age disaggregated)
cod <- strat.fun(setdet = setdet, lf = lf, ag = ag, data.series = "Campelen", 
                 program = "strat2 & strat1", species = 438, survey.year = 1998:2012, 
                 season = "spring", NAFOdiv = "3P", sex = c("male","female","unsexed"), 
                 length.group = 1, group.by = "length & age")

This doesn’t include code for plotting these results

RStrap Explorer

A dashboard was created using the shiny, flexdashboard, plotly, and crosstalk packages to dynamically run and explore RStrap results
Four primary pages:
1. Survey Indices contains stock level estimates of biomass and abundance
2. Age & Length Distributions contains length and age frequency plots
3. Recruitment displays recruitment indices
4. Help provides additional context to the survey data and analysis

RStrap Explorer

Tag Explorer

For some data-rich stocks, mark and recapture studies are carried out to estimate movement, growth rate, natural mortality, etc.
There is a long-standing tagging program for Northern cod
- >60 years, >60,000 records, 1,000 - 10,000 tags deployed annually

Photo: John Brattey

Tag Explorer

Spatial explorations of these data have been limited since the seminal work by Taggart et al. 1995

Nearly 500 pages!

Tag Explorer

A dashboard was created using the shiny, shinydashboard, and leaflet packages to dynamically explore this large data-set
Contains
- One primary page, Main map, where release and recovery locations are mapped
- A series of summary pages where these data are summarized

Tag Explorer

NCAM Explorer

Integrated stock assessment models are becoming more common
Advances in computational power and methods \(\rightarrow\) possibilities
Requires a solid understanding of the inputs, methods and outputs
- Understanding is a prerequisite for communication

Process equations

Stochastic cohort model with a plus group to model the unobserved states: \[\log(N_{a,y}) = \left\{\begin{matrix} \log(N_{a-1,y-1}) - Z_{a-1,y-1} + \delta_{a,y}, & a < A \\ \log\{N_{a-1,y-1}\exp(-Z_{a-1,y-1}) + N_{a,y-1}\exp(-Z_{a,y-1})\} + \delta_{a,y}, & a = A. \end{matrix}\right. \]

The ages are 1-10+ and years are 1975-2015. \(Z_{a,y} = F_{a,y} + M_{a,y}\), where \(M_{a,y} = 0.2\) is the base case assumption.

Recruitments, \(N_{1,1}, ... N_{1,Y}\), are treated as uncorrelated lognormal random variables \[\log(N_{1,y}) \overset{iid}\sim N(r, \sigma_{r}^2).\]

Catches are modeled using the Baranov catch equation, \[C_{a,y} = N_{a,y}\{1 - \exp(-Z_{a,y})\}F_{a,y}/Z_{a,y}.\]

Fishing moralities are modeled as a stochastic process, with \[Cov\{\log(F_{a,y}),\log(F_{a-j,y-k})\} = \frac{\sigma_{F}^2 \varphi_{F,a}^j \varphi_{F,y}^k}{(1-\varphi_{F,a}^2)(1-\varphi_{F,y}^2)}.\]

Observation equations

The model predicted catch for survey \(s\) is \[\log(I_{s,a,y}) = \log(q_{s,a}) + \log(N_{a,y}) - t_{s,y}Z_{a,y} + \varepsilon_{s,a,y}, ~~ \varepsilon_{s,a,y} \overset{iid}\sim N(0, \sigma_{s,G(a)}^2).\]

Survey variance was split out and self-weighted by age groups 1-3, 4-7, and 8-10+

Total catch and age compositions were treated separately. Total catch was modeled as lognormal, \[\log(C_{obs,y}) = \log(C_{y}) + \varepsilon_{C,y}, ~~ \varepsilon_{C,y} \overset{iid}\sim N(0, \sigma_{C}^2)\] Age compositions were modeled as multiplicative logistic normal with a censored component for zero’s

NCAM Explorer

Challenge: the data-rich case of Northern cod
- Assessed using an age-structured, state-space assessment model, called NCAM, which integrates:
  - Research vessel autumn trawl surveys (1983-present)
  - Sentinel fishery surveys (1995-present)
  - Inshore acoustic surveys (1995-2009)
  - Fishery catch-at-age compositions and partial fishery landings (1983-present)
  - Tagging data (1983-present)

NCAM Explorer

Communication: standard approach involves compiling and presenting static documentation

Code

\(\rightarrow\)

Plots & Tables

\(\rightarrow\)

Documentation

Involves a lot of copy-pasting
Important details may not be visible
Linear format may be the best way to explore ideas

NCAM Explorer

A dashboard was created using the flexdashboard, plotly, and crosstalk packages to explore and communicate results from NCAM

Code

\(\rightarrow\)

HTML

Contains a series of pages with plots and tables typically presented at assessment meetings